The top three mapping errors

Top three mapping errors

Anyone that’s been involved in auditing the data transfer process will know that finding mapping inconsistencies can be like trying to find a needle in a haystack. Not only is it a gruelling task for the individuals involved, at a business level it can be incredibly costly and time consuming. And if errors aren’t picked up quickly, competitive performance could be impacted, costing your business large sums of money.  

 

It’s so important to be confident that all your systems are talking to each other correctly, and transferring the data as dictated by your pricing model. However, keeping on top of all the business logic that’s put in place over time, to the changes to PCWs journeys and question sets, can all be incredibly complicated. What we hear time and time again is that maintaining a complex network of systems, databases and enrichment services is far from straightforward.  

 

More often than not, we find that firms don’t have the visibility they need to mitigate against potential mapping errors, therefore preventing them from having 100% certainty in the reliability of the data transfer processes and the optimisation of the customer journey. That lack of visibility can also leave them blind to potential opportunities hidden in the mappings haystack. 

 

For just over six months, we have been helping our clients remove the margin of error when it comes to mappings, using our model that quickly and simply identifies mappings issues and opportunities, saving brands time and money. Over that time, we have identified some common problem areas, and in this blog, we have laid out the top three areas that insurers may need to pay close attention to. 

 

1. Type classification 

Whether its car or van body type, modified or factory standard, we commonly see issues stemming from misclassification. This is also common in home for property type where discrepancies are rife. 

Vans modified to be a camper, 4×4’s classified as estate cars or semi-detached properties classed as terraced crop up often. 

 

Our Head of Innovation, Tim Stout, comments: “When type classifications occur, the prices generated aren’t truly representative of the risk that’s being quoted for. For some brands, the impact could be minor, only affecting a small proportion of new business volume, however for others the impact could be significant and have a tangible effect on the bottom line. The beauty of our service is that we can size the impact of each mapping error, allowing our clients to prioritise the work they need to do to resolve the inconsistencies.” 

 

2. Mileage

To pick an example, PCW questions for vehicle mileage (both personal and for business) can be worded slightly differently and the data provided on the back end can be calculated in different ways. Some provide drop down options in 1k bandings whereas others have free type ‘enter miles per year’. 

 

One PCW will ask: ‘How many miles does this car cover each year? How much of your total annual mileage is for business purposes? Total mileage minus business mileage gives you an accurate personal to business split.’ Whereas another will ask: ‘What’s your total annual mileage (including business mileage)? Commonly here the personal vs business mileage will be calculated with an 80/20 split.’ 

 

Without keeping a keen eye on differences and evolutions in PCW sets, this can prove a key problem area for mapping inconsistencies.  

 

3. NCD

Generally speaking, we see two main behaviours for NCD, capping at 9yrs across the board (a standard mapping for a leading software house) or capping based on the maximum PCW drop down value (20+ or 25+). 
 

These rules can result in lost data for customers with more NCD than the rule allows, and an inability to measure any positive impact on loss for those customers. Customers may get rated differently when getting quotes from more than one PCW. Ultimately if you don’t have the data, you will never be able to ascertain if there were opportunities to sharpen your rates. 
 

Our Data Mapping Diagnostic is designed to identify and resolve mapping inconsistencies between the PCW journey and your back-end pricing database. This service can be complimented by our Question Set Monitor, which provides a regular and concise view of questions, available options and answers exactly as they appear on each of the big four PCWs.   

 

Together these products provide a powerful tool, enabling you to improve your direct and PCW performance by identifying issues that are affecting the accuracy of your pricing, as well as hidden opportunities, allowing you to focus on delivering the results you want to see.  

 

 


 

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Our two newest products will make the pain of identifying mapping errors a thing of the past by quickly and efficiently finding them and helping you stay ahead of the game when it comes to question sets.  

 

Our Data Mapping Diagnostic is designed to identify and resolve mapping inconsistencies between the PCW journey and your back-end pricing database, while our Question Set Monitor provides a regular and concise view of questions, available options and answers exactly as they appear on each of the big four PCWs.  

 

Together these products provide a powerful tool, enabling you to improve your direct and PCW performance by identifying issues that are affecting the accuracy of your pricing, allowing you to focus on delivering the results you want to see. 

 

Take a step towards having 100% certainty in the reliability of your data transfer processes and the optimisation of your end-to-end journey; click here to book a call with a member of our team today. 

 

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Source: www.consumerintelligence.com

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